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# How to Count Special Characters in a Column in Pandas
- URL: https://datascientyst.com/how-to-count-special-characters-in-a-column-in-pandas/
- Published: 2025-02-17T22:33:20.000Z
- Updated: 2025-02-17T22:33:20.000Z
- Author: John D K
- Tags: count()

In this guide, I'll show you **how to count special characters in a column using Pandas.** Whether you want to count special characters row-wise or in the entire column or a single column, these methods will help.

**(1) Count special characters in each row**

```python
df['column_name'].str.count(r'[^a-zA-Z0-9\s]')

```

**(2) Count total special characters in the entire column**

```python
df['column_name'].str.count(r'[^a-zA-Z0-9\s]').sum()

```

**(3) Count occurrences of a specific special character (e.g., `@`)**

```python
df['column_name'].str.count(r'@').sum()

```

**(4) Count the chars by difference of the lengths**

```python
df['text'].str.len() - df['text'].str.replace(r'.', '').str.len()

```

![](https://datascientyst.com/content/images/2025/02/how-to-count-special-characters-in-a-column-in-pandas.webp)

## 1: Example DataFrame

Let's create a sample DataFrame with text values:

```python
import pandas as pd

data = {
    'text': ['Hello@World!', 'Python#Pandas$', 'Data&Science*', 'Special_Chars%']
}

df = pd.DataFrame(data)

```

### **Output:**

|   | text            |
| - | --------------- |
| 0 | Hello@World!    |
| 1 | Python#Pandas$  |
| 2 | Data&Science\*  |
| 3 | Special\_Chars% |

## 2: Count Special Characters in Each Row

To count special characters in each row, use `.str.count()` with a regex pattern:

```python
df['special_char_count'] = df['text'].str.count(r'[^a-zA-Z0-9\s]')

```

### **Output:**

|   | text            | special\_char\_count |
| - | --------------- | -------------------- |
| 0 | Hello@World!    | 2                    |
| 1 | Python#Pandas$  | 2                    |
| 2 | Data&Science\*  | 2                    |
| 3 | Special\_Chars% | 1                    |

## 3: Count Total Special Characters in Column

To count the total number of special characters across all rows:

```python
total_special_chars = df['text'].str.count(r'[^a-zA-Z0-9\s]').sum()

```

### **Output:**

```
8

```

## 4: Count Occurrences of a Specific Special Character (e.g., `@`)

If you need to count how many times a specific character (like `@`) appears in the column:

```python
at_count = df['text'].str.count(r'@').sum()
at_count = df['text'].str.count(r'@')

```

### **Output:**

```
1

```

and

```
0    1
1    0
2    0
3    0

```

## 5: Count Special Characters Across Multiple Columns

If you want to check special characters in multiple text columns:

```python
cols = ['text']  # Add more columns if needed
df['special_char_count'] = df[cols].apply(lambda x: x.str.count(r'[^a-zA-Z0-9\s]')).sum(axis=1)

```

## 6: Count number of dots in column

As an alternative solution we can remove the characters from the column and get the difference from the original length:

```python
df['text'].str.len() - df['text'].str.replace(r'.', '').str.len()

```

## **Conclusion**

This guide covered multiple ways to count special characters in Pandas, including:

- Counting special characters in each row
- Summing special characters across the entire column
- Finding occurrences of specific special characters

These methods are useful for **text processing, data cleaning, and validation tasks.**

## **Resources**

- [Pandas .str.count() Documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.count.html?ref=datascientyst.com)
- [Regular Expressions in Python](https://docs.python.org/3/library/re.html?ref=datascientyst.com)
- [Pandas String Operations](https://pandas.pydata.org/docs/user%5Fguide/text.html?ref=datascientyst.com)
- [How to count special chars in column in Pandas?](https://stackoverflow.com/questions/59687650/how-to-count-special-chars-in-column-in-pandas?ref=datascientyst.com)